Traditionally, optimizing the deployment of cellular networks has required a significant amount of human effort. For example, deploying a base station or a network of base stations typically requires detailed planning beforehand and afterwards in order to optimize the performance of the base stations. Initial planning often includes tasks to analyze the proposed network to determine settings such as power levels, antenna tilts, sectorization patterns, and the like. Additional labor-intensive tasks are undertaken, such as drive tests, to further optimize the network after deployment. Drive tests are typically people driving around the area surrounding the deployed base stations in order to test their performance at various locations. This then leads to further base station optimizations to address problem areas or performance issues.
The labor-intensive nature of these optimization tasks is time consuming and expensive. Automation (or elimination) of some or all of these tasks would therefore provide a savings in time and cost. Note that automation of these tasks does not necessarily require that the same tasks be performed, only that the optimization goals are achieved via a greater level of automation that is traditionally employed. For example, some level of automation can replace the need for drive tests by determining the needed information through other means.
Some level of optimization can be provided by conventional networks, but such optimization has proven inadequate and relatively slow. The types of network automation provided can include self-healing, self-optimization, and self-configuration. Self-healing provides for automated detection and recovery from faults in a network, often from hardware or software issues. Self-optimization provides for automated optimization of a network based on various performance metrics. Self-configuration provides for automating configuration settings such that human intervention is reduced. The more general term of Self-organizing Networks (i.e., SoN) is used herein to refer to these automated tasks.
With base stations being deployed at an increasing density and with the increasing relevance of small cells, the automation of many aspects of base station deployment continues to increase in importance. This occurs not only because there are physically more base stations that are being deployed but because denser base station deployments create increased complexities when deploying new base stations.
It is already appreciated by the cellular industry that SoN is and will continue to become an increasingly important aspect of deploying networks. The majority of the effort for cellular SoNs, however has focused on initial deployment tasks through base station parameter configuration. There have been some additional efforts directed to slowly varying parameters to optimize performance. The slow variations may occur due to traffic patterns, fault conditions, or time of day, among other things. Here, the term “slowly varying” refers to time frames measured in hours or days. Such long time frames are conventionally needed because a base station conventionally does not have access to real time or near real time knowledge of the conditions of its neighboring base stations.
Accordingly, there exists a need for methods, systems, and computer program products for providing a rapidly self-organizing cellular communications network.